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Databricks Databricks-Certified-Data-Engineer-Associate Exam Syllabus Topics:

Section	Weight	Objectives
Data Pipeline Architecture	15-20%	- Understand ELT vs ETL patterns - Implement incremental data processing - Design data pipelines for batch and streaming - Monitor and optimize pipeline performance
Spark SQL and DataFrames	15-20%	- Aggregate and group data - Write and execute Spark SQL queries - Handle null values and data quality - Join and union DataFrames
Apache Spark Data Processing Fundamentals	20-25%	- Use Spark SQL for data processing - Work with structured data types (arrays, maps, structs) - Apply transformations and actions on DataFrames - Create and use Spark DataFrames
Lakehouse Platform Concepts	10-15%	- Describe key Databricks Lakehouse platform components - Understand the Lakehouse architecture and its benefits - Explain data governance and security concepts
Python for Data Engineering	10-15%	- Work with Spark APIs in Python - Use PySpark for data processing - Implement user-defined functions (UDFs)
Delta Lake Fundamentals	20-25%	- Understand ACID transactions and time travel - Explain Delta Lake features and benefits - Create and manage Delta tables - Write to and read from Delta tables

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q190-Q195):

NEW QUESTION # 190
In which of the following file formats is data from Delta Lake tables primarily stored?

A. Delta
B. A proprietary, optimized format specific to Databricks
C. Parquet
D. CSV
E. JSON

Answer: C

NEW QUESTION # 191
A data engineer needs to use a Delta table as part of a data pipeline, but they do not know if they have the appropriate permissions. In which location can the data engineer review their permissions on the table?

A. Catalog Explorer
B. Jobs
C. Dashboards
D. Repos

Answer: A

Explanation:
https://docs.databricks.com/en/delta-live-tables/tutorial-pipelines.html

NEW QUESTION # 192
Which of the following code blocks will remove the rows where the value in column age is greater than 25 from the existing Delta table my_table and save the updated table?

A. DELETE FROM my_table WHERE age <= 25;
B. SELECT * FROM my_table WHERE age > 25;
C. DELETE FROM my_table WHERE age > 25;
D. UPDATE my_table WHERE age > 25;
E. UPDATE my_table WHERE age <= 25;

Answer: C

Explanation:
The DELETE command in Delta Lake allows you to remove data that matches a predicate from a Delta table.
This command will delete all the rows where the value in the column age is greater than 25 from the existing Delta table my_table and save the updated table. The other options are either incorrect or do not achieve the desired result. Option A will only select the rows that match the predicate, but not delete them. Option B will update the rows that match the predicate, but not delete them. Option D will update the rows that do not match the predicate, but not delete them. Option E will delete the rows that do not match the predicate, which is the opposite of what we want. References: Table deletes, updates, and merges - Delta Lake Documentation

NEW QUESTION # 193
Which of the following describes a benefit of creating an external table from Parquet rather than CSV when using a CREATE TABLE AS SELECT statement?

A. Parquet files can be partitioned
B. Parquet files will become Delta tables
C. Parquet files have the ability to be optimized
D. Parquet files have a well-defined schema
E. CREATE TABLE AS SELECT statements cannot be used on files

Answer: D

NEW QUESTION # 194
A data engineering team needs to incrementally ingest customer transactions from a SaaS app into Data Intelligence Platform with:
- Built-in change data capture (including updates and deletes)
- Automatic schema evolution
- Serverless execution with retries and minimal maintenance
- OAuth support and basic monitoring
Which solution meets all of these requirements?

A. Use Auto Loader on exported SaaS files and an SDP that calls the SaaS APIs, handling CDC, schema changes, OAuth, and retries in your own code.
B. Schedule batch jobs per region that call SaaS REST APIs and write directly to silver tables with custom CDC and schema evolution, and use system tables for ad hoc basic monitoring.
C. Use a Lakeflow Connect standard connector, implement MERGE-based CDC, schema evolution, and retries in workflows, and set up a dedicated monitoring dashboard.
D. Use a Lakeflow Connect managed connector for the SaaS app with built-in CDC, schema evolution, OAuth, and serverless compute with retries, then process downstream with Lakeflow Spark Declarative Pipelines.

Answer: D

Explanation:
Lakeflow Connect managed connectors provide source-specific OAuth authentication, built-in incremental ingestion and CDC, automatic schema evolution, automated retries, serverless execution, and integrated monitoring with minimal custom maintenance.

NEW QUESTION # 195
......

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